Description

You will design, build, and maintain scalable data platforms for large-scale ingestion, storage, and processing of structured and unstructured data.

Responsibilities

• Design and implement scalable data platforms and distributed systems to support self-service analytics and real-time processing.
• Build and maintain efficient ETL pipelines and real-time data streaming workflows using SQL and Python.
• Develop data models, schemas, and transformation pipelines optimized for query performance and cost efficiency.
• Deploy machine learning pipelines using MLOps best practices to integrate data engineering with ML workflows.
• Automate data pipeline deployments and infrastructure management using CI/CD tools and Terraform.

Required Skills

• 5+ years of experience in data engineering and large-scale data processing.
• Strong proficiency in SQL for data transformation, optimization, and advanced analytics.
• Expertise in Python, Java, Scala, or Go with understanding of functional and object-oriented paradigms.
• Experience with cloud-based data engineering on GCP, AWS, or Azure.
• Hands-on experience with distributed computing frameworks.
• Proficiency with Kafka or equivalent real-time data streaming technologies.
• Experience with infrastructure automation using Terraform and Jenkins.
• Solid understanding of data modeling, schema design, and data governance.
• Knowledge of MLOps and integrating data engineering with ML pipelines.

Preferred Skills

• Experience with Apache Beam, knowledge graphs, or GraphQL APIs.
• Background in retail, customer classification, or personalization systems.
• Experience in the production of consumer goods.

Key Skills

Education

Any Gradute